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As we invest in the next generation of our technology platform on Google Cloud, we are seeking a driven
AI/ML Solutions Engineer
to help harness the power of artificial intelligence—automating complex workflows and deploying intelligent tools that improve outcomes for members, clients, and internal teams. This is a hybrid position. Position Overview The AI/ML Solutions Engineer will be a hands-on technical contributor and subject matter expert, working closely with the CFO’s office, operations, and business stakeholders. This is a high-impact, mid-level role for an engineer who is equally comfortable building ML pipelines and translating business needs into production-ready AI systems, with a strong focus on
Generative AI and LLM-powered applications
in the health insurance and benefits administration domain. The role also requires adherence to enterprise-grade
security, risk, and compliance frameworks , including SOC 1, SOC 2, and CMMC-aligned controls. Key Responsibilities Generative AI & LLM Implementation Design, build, and deploy Generative AI solutions using
Google Vertex AI , Gemini APIs, and related GCP services Identify and prioritize high-value use cases (e.g., claims summarization, member communications, eligibility Q&A, document processing, knowledge retrieval) Implement prompt engineering,
RAG (retrieval-augmented generation) , and fine-tuning strategies to ensure accuracy in regulated environments Ensure solutions align with
responsible AI principles , including explainability, auditability, and HIPAA compliance Incorporate
secure prompt handling, data redaction, and model access controls
to prevent data leakage and unauthorized use ML Pipeline Development, Infrastructure & Security Build and maintain scalable, end-to-end ML pipelines (data ingestion → deployment → monitoring) Leverage GCP tools such as
Vertex AI Pipelines, BigQuery ML, Dataflow, and Cloud Composer Establish
MLOps practices
(CI/CD, model versioning, monitoring, automated retraining) Implement
security-by-design principles
across ML pipelines, including: Data encryption (at rest and in transit) Identity and Access Management (IAM) with least-privilege access Secure API design and service authentication Logging, monitoring, and audit trails for all ML systems Align infrastructure and workflows with
SOC 1 / SOC 2 controls
(security, availability, confidentiality) and
CMMC practices
where applicable Partner with security and compliance teams to support audits, evidence collection, and control validation Business Stakeholder Collaboration Partner with finance, operations, and leadership to identify AI opportunities that reduce administrative burden and improve outcomes Translate business problems into well-defined AI/ML solutions Communicate technical concepts clearly to non-technical stakeholders Serve as an internal advocate for AI/ML adoption,
secure development practices , and responsible AI governance Required Qualifications Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or related field 3–5 years of experience in AI/ML engineering, data science, or related roles Hands-on experience with
Google Cloud Platform (GCP) , including Vertex AI,
BigQuery , Dataflow, and Cloud Functions Experience with
Generative AI / LLMs , prompt engineering, and RAG pipelines Strong Python skills and experience with ML frameworks (TensorFlow, PyTorch, scikit-learn, XGBoost) Working knowledge of
cloud security best practices , including IAM, encryption, secrets management, and secure SDLC Preferred Qualifications xywuqvp
Experience supporting or operating within
SOC 1 and SOC 2 compliant environments Familiarity with
CMMC (Cybersecurity Maturity Model Certification)
practices and control frameworks Relevant certifications such as: Certified Information Systems Security Professional ( CISSP ) Certified Cloud Security Professional ( CCSP ) CompTIA Security+ Google Professional Cloud Security Engineer CMMC-related training or certification Experience in healthcare, insurance, TPA operations, or other regulated environments Familiarity with
HIPAA
and privacy-preserving ML techniques Experience with claims, eligibility, or benefits data Experience building internal AI tools (chatbots, document Q&A systems, automation agents) Strong data engineering fundamentals, including: SQL (PostgreSQL, MySQL) NoSQL (MongoDB, Cassandra) Data warehousing (BigQuery, Snowflake) Data cleaning, transformation, and feature engineering Real-time/stream processing systems Basic DevOps (Docker, Kubernetes) Offer Competitive compensation commensurate with experience Flexible engagement structure (contract, remote/hybrid options) High-visibility role with direct exposure to executive leadership Opportunity for extension or conversion to a permanent position
Sprachkenntnisse
- English
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